Abstract
Three classes of statistical techniques used to solve image segmentation and labelling problems are reviewed: (1) supervised and unsupervised pixel classification, (2) exploitation of the probability distribution map as a way to model image structure, (3) Markov random field modelling combined with MAP statistical classification. Diverse examples illustrate the potential of the three approaches that are described as generic methods belonging to a common framework for image segmentation/labelling
| Original language | English |
|---|---|
| Title of host publication | WCC 2000 - ICSP2000, World Computer Conference 2000 - 5th International Conference on Signal Processing Proceedings; Beijing, China III/III ; Aug 2000. |
| Editors | Yuan Baozong, Tang Xiaofang |
| Publisher | 16 th World Computer Conference 2000 (WCC 2000) - 5th International Conference on Signal Processing (ICSP2000) Proceedings, Vol. III, pp. 2103-2110, Beijing, China. |
| Pages | 2103-2110 |
| Number of pages | 8 |
| Volume | III |
| Publication status | Published - Aug 2000 |
Bibliographical note
16 th World Computer Conference 2000 (WCC 2000) - 5th International Conference on Signal Processing (ICSP2000) Proceedings, Vol. III, pp. 2103-2110, Beijing, China.Series editor: Yuan Baozong, Tang Xiaofang